An out-of-focus state or a camera shake during capturing causes a blur in an input image. By performing image restoration processing to a blurred input image, a high-definition output image can be obtained. However, the image restoration processing emphasizes high-frequency component of the input image, which causes noise included in the input image to be also amplified. Therefore, it is difficult to obtain a good output image through the mage restoration processing when noise is included in the input image.
Conventionally, a method of designing a restoration function using noise information has been proposed in order to prevent noise from being emphasized by the image restoration processing. Generally, in the image restoration processing, an inverse function of a degradation function H (u, v) is used as a restoration function M (u, v), as shown in Equation 1 below
                    [                  Math          .                                          ⁢          1                ]                                                                      M          ⁡                      (                          u              ,              v                        )                          =                  1                      H            ⁡                          (                              u                ,                v                            )                                                          Equation        ⁢                                  ⁢        1            
An image degraded due to a blur etc. includes less high-frequency component. That is, emphasizing the high-frequency component can reduce the blur. However, emphasizing the high-frequency component as described above causes the noise to be emphasized as well.
In a method disclosed in Patent Literature 1, a restoration function M (u, v) is designed as shown in Equation 2 below by using a spectral density of noise, Sn (u, v), and a spectral density of an approximately-ideal image, Sf (u, v).
                    [                  Math          .                                          ⁢          2                ]                                                                      M          ⁡                      (                          u              ,              v                        )                          =                              1                          H              ⁡                              (                                  u                  ,                  v                                )                                              ×                                                                                      H                  ⁡                                      (                                          u                      ,                      v                                        )                                                                              2                                                                                                              H                    ⁡                                          (                                              u                        ,                        v                                            )                                                                                        2                            +                                                Sn                  ⁡                                      (                                          u                      ,                      v                                        )                                                  /                                  Sf                  ⁡                                      (                                          u                      ,                      v                                        )                                                                                                          Equation        ⁢                                  ⁢        2            
In Equation 2, the restoration function is designed based on a fact that an approximate amount of the noise is estimated based on a ratio of the spectral density of noise, Sn (u, v), to the spectral density of an approximately-ideal image, Sf (u, v). That is, in the restoration function shown in Equation 2, a degree of restoration by the image restoration processing is adjusted based on the amount of noise estimated. Specifically, when less noise is included (when Sn (u, v)/Sf (u, v) is very small), the restoration function shown in Equation 2 is approximately the same as the general restoration function (the inverse function of the degradation function) shown in Equation 1, whereby the image can be sufficiently restored. In contrast, when much noise is included (when Sn (u, v)/Sf v) is great), the restoration function shown in Equation 2 shows a smaller degree of restoration than the general restoration function shown in Equation 1, whereby the noise is restrained from being emphasized by the image restoration processing.
Furthermore, a method of reducing noise prior to and after the image restoration processing has been proposed (for example, see Patent Literature 2). FIG. 10 shows the conventional image restoration processing disclosed in Patent Literature 2.
In the conventional image restoration processing shown in FIG. 10, firstly, a first noise reduction filter processing 30 that reduces noise in an input image is performed, based on exposure information detected in an exposure information detection 40. Next, restoration processing 50 is performed to the noise-reduced image. Performing the first noise reduction filter processing 30 that reduces noise prior to the restoration process 50 as described above restrains the noise from being emphasized by the restoration processing 50. The noise is further reduced by performing a second noise reduction filter processing 60 after the restoration processing 50.